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This work addresses the problem of using Unmanned Aerial Vehicles (UAV) to deploy a wireless aerial relay communications infrastructure for stations scattered on the ground. In our problem, every station in the network must be assigned to a single UAV, which is responsible for handling all data transfer on behalf of the stations that are assigned to it. Consequently, the placement of UAVs is key to achieving both network coverage and the maximization of the aggregate link capacities between UAVs and stations, and among the UAVs themselves. Because the complexity of this problem increases significantly with the number of stations to cover, for a given fixed number p of available UAVs, we model it as a single allocation p-hub median optimization problem, and we propose a hybrid metaheuristic algorithm to solve it. A series of numerical experiments illustrate the efficiency of the proposed algorithm against traditional optimization tools, which achieves high-quality results in very short time intervals, thus making it an attractive solution for real-world application scenarios.

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The hub location problem has gained attention over the last decades. In telecommunications network, a hub is a place of concurrence in where the work of the network is centralized with the purpose of delivering out the data that arrives from one or more directions to other destinations. There are different versions of the hub location problem depending upon (1) the existence or not of restrictions on the capacity related to the volume of flow a hub is allowed to support, (2) the existence or not of a set‐up cost associated with selecting any node as a hub, etc. In these types of configurations, the hubs serve as connection point between 2 installations, allowing, in this way, to replace a large amount of direct connections between all pair of the nodes, therefore, minimizing the total transportation cost of the network. Thus, this work proposes a 2‐stage metaheuristic based on the combination of biased‐randomized technique with an iterated local search framework for solving the uncapacitated single allocation p‐hub median problem, with computational results that validate the methodology for large‐size instances from the literature.